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    • Keywords


      Neuronal networks; scale-free network; synapses; learning; logistic map.

    • Abstract


      We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution. Then we study the dynamics of the structure of a formal neural network. For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of the real neural network of 𝐶. elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of model parameters.

    • Author Affiliations


      Kiran M Kolwankar1 2 Quansheng Ren2 3 Areejit Samal2 4 Jürgen Jost2 5

      1. Department of Physics, Ramniranjan Jhunjhunwala College, Ghatkopar (W), Mumbai 400 086, India
      2. Max Planck Institute for Mathematics in the Sciences, Inselstr. 22, 04103 Leipzig, Germany
      3. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
      4. Laboratoire de Physique Théorique et Modèles Statistiques, CNRS and Univ Paris-Sud, UMR 8626, F-91405 Orsay, France
      5. Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
    • Dates

  • Pramana – Journal of Physics | News

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      Posted on July 25, 2019

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